Statistical Analysis Of Parameters Of Organic Peroxides And Its Safety Research

Safety science has been developing rapidly for many years in China. The GB/T13745-2009"subject classification and code" expanded five grade-2subjects which include safety material science. The research of safety material science is still in the initial exploratory stage. This paper selected a series of organic peroxides as object to explore the research contents and methods of safety material science.Based on the collection and access to relevant literature, systematic review was given to the research status of the safety material science and organic peroxides at home and abroad. The parameter data of organic peroxides were count and analyzed by correlation analysis, factor analysis, cluster analysis, discriminant analysis and Copeland score sort. Five common factors affecting the safety of organic peroxides and classification results based on their safety were obtained. Then the main physical parameters were predicted by group contribution method, BP neural network, multiple linear regression, partial least squares and support vector machine. The main research and conclusions in this paper are as follows:(1) The statistics parameter data of72organic peroxides were divided into three categories:molecular composition parameter, molecular structure parameter and physical parameter. Through the correlation analysis among three categories data and the definition of the parameter, few parameters were deleted.(2) By the factor analysis of23parameters of72organic peroxides, five common factors were extracted and respectively defined as combustion and explosion, decomposition, diffusion, pollution and corrosion, and toxicity. Compared to previous studies, the result showed that the five common factors can be a good characterization to the safety of organic peroxides.(3) Based on the result of factor analysis and the integrity of the statistical data,33organic peroxides and their10parameters were selected.28organic peroxides were classified as three categories by the cluster analysis.5unknown types’ organic peroxides were analyzed by discriminant analysis. Then33organic peroxides were sort by Copeland score. The comparison among the results of the three methods demonstrated that there was high consistent.(4) According to combustion and explosion can best represent flash point, a method based on BP neural network and group contribution method to predict the flash point of organic peroxides was designed.50organic peroxides were chosen as training set and16organic peroxides were chosen as testing set. The result showed that the predicted values were in good agreement with literature values. The prediction method didn’t require calculation of the parameters, and the results were reliable.(5) According to previous research, it was known that the reaction hazards of organic peroxides depend on the initial decomposition temperature and heat of decomposition.14organic peroxides were chosen as training set,3organic peroxides were chosen as testing set, and13parameters were determined as the descriptors. The initial decomposition temperature and heat of decomposition were predicted by multiple linear regression, partial least squares and support vector machine. The effectiveness of three methods were compared and verified. Finally, the predicted models of PLS or SVM had a higher linear fit with literature data and a good predictability.